@Namespace(value="cv::xfeatures2d") @Properties(inherit=opencv_xfeatures2d.class) public class MSDDetector extends Feature2D
The algorithm implements a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of "contextual self-dissimilarity" reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, it extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator| Constructor and Description |
|---|
MSDDetector()
Default native constructor.
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MSDDetector(long size)
Native array allocator.
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MSDDetector(Pointer p)
Pointer cast constructor.
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| Modifier and Type | Method and Description |
|---|---|
static MSDDetector |
create() |
static MSDDetector |
create(int m_patch_radius,
int m_search_area_radius,
int m_nms_radius,
int m_nms_scale_radius,
float m_th_saliency,
int m_kNN,
float m_scale_factor,
int m_n_scales,
boolean m_compute_orientation) |
MSDDetector |
position(long position) |
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public MSDDetector(long size)
Pointer.position(long).public MSDDetector(Pointer p)
Pointer.Pointer(Pointer).public MSDDetector position(long position)
@opencv_core.Ptr public static MSDDetector create(int m_patch_radius, int m_search_area_radius, int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN, float m_scale_factor, int m_n_scales, @Cast(value="bool") boolean m_compute_orientation)
@opencv_core.Ptr public static MSDDetector create()
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